Paper Info
Title
Optimal Sampling of Water Distribution Network Dynamics using Graph Fourier Transform.
Abstract
Water Distribution Networks (WDNs) are critical infrastructures that ensure safe drinking water. One of the major threats is the accidental or intentional injection of pollutants. Data collection remains challenging in underground WDNs and in order to quantify its threat to end users, modeling pollutant spread with minimal sensor data is can important open challenge. Existing approaches using numerical optimisation suffer from scalability issues and lack detailed insight and performance guarantees. Applying general data-driven approaches such as compressed sensing (CS) offer limited improvements in sample node reduction. Graph theoretic approaches link topology (e.g. Laplacian spectra) to optimal sensing locations, it neglects the complex dynamics. In this work, we introduce a novel Graph Fourier Transform (GFT) that exploits the low-rank property to optimally sample junction nodes in WDNs. The proposed GFT allows us to fully recover the full network dynamics using a subset of data sampled at the identified nodes. The proposed GFT technique offers attractive improvements over existing numerical optimisation, compressed sensing, and graph theoretic approaches. Our results show that, on average, with nearly 30-40% of the junctions monitored, we are able to fully recover the dynamics of the whole network. The framework is useful beyond the application of WDNs and can be applied to a variety of infrastructure sensing for digital twin modeling.
Year
DOI
Venue
2019
10.1109/tnse.2019.2941834
IEEE Transactions on Network Science and Engineering
DocType
Volume
Citations 
Journal
abs/1904.03437
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Zhuangkun Wei144.21
Alessio Pagani201.35
Guangtao Fu311.04
Ian Guymer410.70
Wei Chen514048.88
Julie McCann600.34
Weisi Guo755060.46